How to learn machine learning in Python is a very popular topic: with the rise of artificial intelligence, programmers have been able to do everything from beating human masters at Go to replicating human-like speech. At the foundation of this fantastic technological advance are programming and statistics principles you can learn.

Excel can be a powerful tool for data exploration and analysis when dealing with small data sets, but for anything more complex it often makes more sense to use Python. PyXLL lets you keep the best of both by integrating Python into Excel. You can use Excel as an interactive user interface and use Python to do the data fetching, cleaning and computation.

Python Basics

Before you learn how to run, you have to learn how to walk. Most people who start learning machine learning and deep learning come from a programming background: if you do, you can skip this section. However, if you’re new to programming or you’re new to Python, you’ll want to take a look through this section.

These are instructions for installing Jupyter Notebook, an intuitive interface for Python code. You’ll have all of the important Python libraries you need pre-installed and you’ll be easily able to export out and show all of your work in an easy-to-visualize fashion. I strongly suggest that you use Jupyter as your default tool for Python, and the rest of this learning path assumes that you are.

Statistics Basics

In order to learn machine learning in Python, you not only have to learn the programming behind it — you’ll also have to learn statistics. Here are some resources that can help you gain that fundamental knowledge.

Khan Academy is the largest source of free online education with an array of free video and online courses. This section on Khan Academy will teach you the basic statistics concepts you need to know to understand machine learning, deep learning and more — from mode, median, mean to probability concepts.

This book will delve into Bayesian methods and how to program with probabilities. Combined with your budding knowledge of Python, you’ll be quickly able to reason with different statistical concepts. It’s a book the author gave out for free — and its deeply interactive nature promises to engage you into these new concepts.

Pandas

The main workhorse of data science in Python is the Pandas data science library, an open-source tool that allows for a tabular organization of large datasets and which contains a whole array of functions and tools that can help you with both data organization, manipulation, and visualization. In this section, you’ll be given the resources needed to learn Pandas which will help you to learn machine learning in Python.

Julia Evans, a programmer based in Montreal, has created this simple step-by-step tutorial on how to analyze data in Pandas using noise complaint and bike data. It starts with how to read CSV data into Pandas and goes through how to group data, clean it, and how to parse data.

The official Pandas cookbook involves a number of simple functions that can help you with different datasets and hypothetical transformations you might want to do on your data. Take a look and play with it to extend your knowledge of Pandas.

Data Exploration and Wrangling

Before you can do anything with the data, you’ll want to explore it, and do what is called exploratory data analysis (EDA) — summarize your dataset and get different insights from it so you know where to dig deeper. Fortunately, tools like Pandas are built to give you relevant and surprisingly deep summary insights into your data, allowing you to shape which questions you want to explore next.

By looking through your dataset from afar, you’ll already be able to understand what faults the dataset might have that will keep you from completing your analysis: missing values, wrongly formatted data etc. This is where you can start processing and transforming the data into a form that you want to answer your questions. This is called “data wrangling” — you are cleaning the data and making sure that it is able to answer all of your questions in this step.

This article from Datacamp goes through all of the nuts and bolts functions you need in order to take a slightly deeper look at your data. It covers topics ranging from summarization of data to understanding how to select certain rows of data. It also goes into basic data wrangling steps such as filling in null values. There are interactive embedded code workspaces so you can play with the code in the article while you are digesting its concepts.

This blog article from Springboard is filled with code examples that describe how you can filter data, detect and drop invalid/null values from your dataset, how to group data such that you can perform aggregated analyses on different groups of data (ex: doing an analysis of survival rate on the Titanic by gender or passenger class) and how to handle time series data in Python. Finally, you’ll learn how to export out all of your work in Python so that you and others can play around with it in different file formats such as the Excel-friendly CSV.

This Pandas cheat sheet, hosted on Github, can be an easy, visual way to remember the Pandas functions most essential to data exploration and wrangling. Keep it as a handy reference as you go out and practice some more.

Data Visualization

Data exploration and data visualization work together hand-in-hand. Learning how to visualize data in different plots can be important is seeing underlying trends.

This legend of resources on the official matplotlib library (the workhorse library for Python data visualization) will help you understand the theory behind data visualization and how to build basic plots from your data.

The Seaborn library allows people to create intuitive plots that the standard matplotlib library doesn’t cover easily: things like violin plots and box plots. Seaborn comes with very compelling graphics right out of the box.

Introduction to Machine Learning

Machine learning is a set of programming techniques that allow computers to do work that can simulate or augment human cognition without the need to have all parameters or logic explicitly defined.

The following section will delve into how to use machine learning models to create powerful models that can help you do everything from translating human speech to machine code, to beating human grandmasters at complex games such as Go.

It’s important before we get started implementing ideas in code that you understand the fundamentals of machine learning. This section will help you understand how to test your machine learning models, and what statistics you should use to measure your performance. It is an essential cornerstone to your drive to learn machine learning in Python.

This handy visualization will allow you to understand what machine learning is and the basic mechanisms behind it through a visual display of how machines can classify whether a home is in New York or in San Francisco.

This article explains why you need to split your dataset into training and test sets and why you need to perform cross-validation in order to avoid either underfitting or overfitting your data. Does that seem like a lot of jargon to you? The article will define all of these different concepts, and show you how to implement them in code.

Sci-kit Learn

Sci-kit learn is the workhorse of machine learning and deep learning in Python, a library that contains standard functions that help you map machine learning algorithms to datasets.

It also has a bunch of functions that will allow you to easily transform your data and split it into training and test sets — a critical part of machine learning. Finally, the library has many tools that can evaluate the performance of your machine learning models and allow you to choose the best for your data.

You’ll want to make sure you know how to effectively use the library if you want to learn machine learning in Python.

This post introduces a lot of the history and context of the Sci-Kit Learn library and it gives you a list of resources and documentation you can pursue to further your learning and practice with this library.

The official scikit-learn documentation is filled with resources and quick start guides that will help you get started with Scikit-Learn and which will help you entrench your learning.

Regression

Regression involves a breakdown of how much movement in a trend can be explained by certain variables. You can think about it as plotting a Y or dependent variable versus a slew of X or explanatory variables and determining how much of the movement in Y is dependent on individuals factors of X, and how much is due to statistical noise.

There are two main types of regression that we’re going to talk about here: linear regression and logistic regression. Linear regression measures the amount of variability in a dependent factor based on an explanatory factor: you might, for example, find out that poverty levels explain 40% of the variability in the crime rate. Logistic regression mathematically transforms a level of variability into a binary outcome. In that way, you might classify if a name is most likely to be either male or female. Instead of percentages, logistic regression produces categories.

You’ll want to study both types of regression so you can get the results you need.

This Medium tutorial uses the Sci-Kit Learn tools available to implement a logistic regression model. The amount of detail in each step will help you follow along.

Clustering

Another type of machine learning model is called clustering. This is where datasets are grouped into different categories of data points based on the proximity between one point and other groups of points. Mastering clustering is an important part of learning machine learning in Python.

This article from Yhat demonstrates how to do simple K-means clustering across different wine customers. It’ll take your learning in Pandas and Scikit-Learn and combine them into a useful clustering example.

Deep Learning/Neural Networks

Neural networks are an attempt to simulate how the human mind works (on a very simplified level) in computational code. They have been a great advance in artificial intelligence — and while in some ways they are a black box of complex algorithms working in tandem to learn how data generalizes, their practical applications have exponentially multiplied in the last few years. Deep learning encompasses neural networks as well as other approaches meant to simulate human intelligence. They are an important part to learn if you want to learn machine learning in Python.

This short Wired article isn’t a technical tutorial: it’s the recounting of an epic match between a human grandmaster at Go, a game that was supposed to be so complex for computers to win that technology to do so wasn’t supposed to come until around the 2030s. By leveraging the power of neural networks, Google was able to bring AI victory forward some two decades or so. This article should give you a great glimpse at the potential and power of neural networks.

This example-laden tutorial uses the neural networks module in the Scikit-Learn library to build a simple neural network that can classify different types of wine. Follow along and play with the code so you can get a feel for how to build neural networks.

This tutorial from Machine Learning Mastery uses the Python implementation of the Keras library to build slightly more powerful and intricate neural networks. Keras is a code library built to optimize for speed when it came to experimenting with different deep learning models.

Big Data

Big data involves a lot of volume and velocity of data. It’s an amount of data, measured in petabytes, that can’t be processed easily with tools like Pandas, which are based on the processing power of one laptop or computer.

You’ll want to scale out to controlling many processors and servers and passing data through a network to process data at scale. Tools that allow you to map and reduce data between multiple servers and others such as Spark and Hadoop play an important role here. It’s time to take the learning you’ve had before this and apply it to massive data sets! You can’t learn machine learning in Python without dealing with big data.

This blog post will help you get set up with PySpark, a Python library that brings the full power of Spark to you in the Jupyter Notebook format you’ve been used to working in. PySpark can be used to process large datasets that can go all the way to petabytes of data!

This tutorial from Insight goes a little further than installation instructions and gets you working with Spark on a terabyte (that’s 1024 gigabytes!) of Reddit comment data.

Machine Learning Evaluation

Now that you’ve learned a baseline for all of the theory and code you need to learn machine learning in practice, it’s time to learn what metrics and approaches you can use to evaluate your machine learning models.

In this tutorial, you’ll learn about the different metrics used to evaluate the performance of different machine learning approaches. You’ll be able to implement them in Scikit-Learn and Jupyter right away!

This long six-part series (check the end of this blog post for more posts after) goes deep into the theory and math behind machine learning evaluation metrics. You’ll come out of the whole thing with a deeper knowledge of how to measure machine learning models and compare them against one another.

Suggested daily routine

Learning isn’t often a static thing. You need ongoing practice to master a skill. Here’s a suggested learning routine you can implement in your day to make sure you practice and expand your knowledge and learn machine learning in Python.

Here’s my suggested daily routine:

Continue working on something in machine learning at all times

Go to StackOverflow, ask and answer questions

Read the latest machine learning papers, try to understand them

Practice your code whenever you can by looking through Github machine learning repositories

Do Kaggle competitions so you can extend your learning and practice new machine learning concepts

At the end, you’ll have effectively mastered how to learn machine learning in Python!

How to do common Excel and SQL tasks in Python

The code and data for this tutorial can be found in this Github repository. For more information on how to use Github, check out this guide.

Data practitioners have many tools that they use to slice and dice data. Some people use Excel, some people use SQL — and some people use Python. The advantages of using Python are obvious when it comes to certain tasks. You can process much bigger datasets at much faster speeds. You can use open source machine learning libraries built on top of Python. You can easily import and export data in different formats.

Python can become an essential part of any data analyst’s toolbox due to its versatility. However, it can be hard to get started. Most data analysts are probably familiar with either SQL or Excel. This tutorial is structured to help you transfer over skills and techniques from those two programs to Python.

First, let’s get you set up on Python. The easiest way to get started is to use Jupyter Notebook and Anaconda. This visual interface will allow you to plug Python code in and immediately see the output of your results. It’ll make it easy for you to follow along with the rest of this tutorial as well.

I highly recommend using Anaconda, but this beginners guide will also help you with installing Python directly — though that’ll make following this tutorial harder.

Let’s start with the basics: opening up a dataset.

IMPORTING DATA

You can import .sql databases and process them in SQL queries. On Excel, you could double-click a file and then start working with it in spreadsheet mode. In Python, there’s slightly more complexity that comes at the benefit of being able to work with many different types of file formats and data sources.

Using Pandas, a data processing library, you can import a variety of file formats using the read function. A full list of the file formats you can import using this function is in the Pandas documentation. You can import everything from CSV and Excel files to the whole content of HTML files!

One of the biggest advantages of using Python is the ability to be able to source data from the vast confines of the web instead of only being able to access files you’ve downloaded manually. The Python requests library can help you sort through different websites and take data from them while the BeautifulSoup library can help you process and filter the data so you get exactly what you need. Be careful of usage rights issues if you’re going to go down this route.

(Don’t worry if you want to skip this part, you can! The raw csv file is here, and you can download it at will if you’d rather start this exercise without taking data from the web. Or you can git clone the entire repository.)

In this example, we’re going to take a Wikipedia table of countries by their nominal GDP per capita (a technical term that means an amount of income a country earns divided over the number of its population), and use the Pandas library in Python to sort through the data.

First, let’s import the different libraries we need. For more information on how imports work in Python, click here.

We’ll need the Pandas library to process our data. We’ll need the numpy library to perform manipulations and transformations of numeric data. We’ll need the requests library to get HTML data from a website. We’ll need BeautifulSoup to process that data. Finally, we’ll need the regular expression library of Python (re) to change certain strings that will come up as we process the data.

It’s not necessary to know much about regular expressions in Python, but they are a powerful tool you can use to match and replace certain strings or substrings. Here’s a tutorial if you wanted to learn more.

You can copy + paste the code above into your own Anaconda setup, and iterate with it if you want to play with some Python code!

The output from the code below, if you don’t modify it, is what is known as a list of dictionaries.

You’ll notice commas separating bracketed lists of key-value pairs. Each bracketed list represents a row in our dataframe, and each column is represented by the keys within: we are working with a country’s rank, its GDP per capita (expressed as US$), and its name (in ‘Country’).

Thankfully, we don’t need to understand much of that in order to move this data into a Pandas dataframe, a similar way of aggregating data to a SQL table or an Excel spreadsheet. With one line of code, we’ve assigned and saved this data into a Pandas dataframe — as it turns out to be the case, lists of dictionaries are the perfect data format to be converted to a dataframe.

gdp = pd.DataFrame(data)

With this simple Python assignment to the variable gdp, we now have a dataframe we can open up and explore anytime we write out the word gdp. We can add Python functions to that word to create curated views of the data within. For a bit more of an in-depth look at what we just did with the equal sign and assignment in Python, this tutorial is helpful.

TAKING A QUICK LOOK AT THE DATA

Now, if we want to take a quick look at what we’ve done, we can use the head() function, which works very similarly to selecting a few rows in Excel or the LIMIT function in SQL. Use it handily to take a quick look at datasets without loading the whole thing! You can also insert a number within the head function if you want to look at a particular number of rows.

gdp.head()

The output we get are the first five rows of the GDP per capita dataset (the default value of the head function), which we can see are neatly arranged into three columns as well as an index column. Be aware that Python starts indexes at 0 and not 1, such that if you wanted to call up the first value in a dataframe, you’d use 0 instead of 1! You can change the number of rows displayed by adding a number of your choice within the parentheses. Try it out!

RENAMING COLUMNS

One thing you’ll quickly realize in Python is that names with certain special characters (such as $) can become very annoying to handle. We’ll want to rename certain columns, something you can do easily in Excel by clicking on the column name and typing over the old name and something you can do in SQL either with the ALTER TABLE statement or sp_rename in SQL server.

In implementing the above function, we’ll be replacing the column header ‘US$’ with the column header ‘gdp_per_capita’. A quick .head() function call confirms that this change has been made.

DELETING COLUMNS

There’s been some data corruption! If you look at the Rank column, you’ll notice that there are random dashes scattered throughout it. That’s not good, and since the actual number order is disrupted, this makes the Rank column quite useless, especially with the numbered index column that Pandas gives you by default.

Fortunately, deleting a column is easy with a built-in Python function: del. By selecting columns through the use of square brackets appended to the dataframe name.

del gdp['Rank']

Now, with another call to the head function, we can confirm that the dataframe no longer contains a rank column.

CONVERTING DATA TYPES WITHIN COLUMNS

Sometimes, a given data type is hard to work with.This handy tutorial will break down the differences between the different data types in Python in case you need a refresher.

In Excel, you could right-click and find ways of converting columns of data to a different type of data quite easily. You could copy a set of cells rendered by formulas and paste special as values, and you can use formatting options to quickly switch between numbers, dates, and strings.

It’s not as easy in Python to switch between one data type to the other sometimes, but it’s certainly possible.

Let’s first use the re library in Python. We will regular expressions to replace the commas within the gdp_per_capita column so we can more easily work with that column.

The re.sub function essentially takes every comma and replaces it with a blank space. This following tutorial goes into each function of the re library in detail.

Now that we’ve gotten rid of the commas, we can easily convert the column into a numeric one.

gdp['gdp_per_capita'] = gdp['gdp_per_capita'].apply(pd.to_numeric)

Now we can calculate a mean for the column.

We can see that the mean of the GDP per capita column is about $13037.27, something we couldn’t do if the column were classified as strings (which you can’t perform arithmetic operations on). We can now do all sorts of calculations on the GDP per capita column that we weren’t able to do before — including filtering the columns by different values and determining what percentile rank values are for the column.

SELECTING/FILTERING DATA

The basic need of any data analyst is to slice and dice a large dataset into actionable insights. In order to do that, you have to go through a subset of the data you have: this is where selecting and filtering data is very helpful. In SQL, this is accomplished with a mix of SELECT and different other functions, while in Excel, this can be done by dragging and dropping through data and implementing filters.

Using the Pandas library, you can quickly filter down with different functions or queries.

Let’s, as a quick proxy, only show countries that have a GDP per capita above $50,000.

This is how to do it:

gdp50000 = gdp[gdp['gdp_per_capita'] > 50000]

We assign a new dataframe with a filter that takes a column and creates a boolean variable — this function above essentially says “create a new dataframe for which there is a GDP per capita above 50000”. Now we can display gdp50000.

And now we see that there are 12 countries with a GDP above 50000!

Now let’s select only rows that belong to a country that start with s.

We can now display a new dataframe containing only countries that start with s. A quick check with the len function (a life-saver for counting the number of rows in a dataframe!) indicates that we have 25 countries that fit the bill.

Now what if we want to chain those two filter conditions together?

Here’s where chained filtering comes in handy. You’ll want to understand how this works before filtering with multiple conditions. You’ll also want to understand the basic operators in Python. For the purposes of this exercise you just need to know that ‘&’ stands for AND — and that ‘ | ‘ stands for OR in Python. However, with a deeper understanding of all basic operators, you can easily manipulate data with all sorts of conditions.

Let’s go ahead and work on filtering countries that both start with ‘S’ AND that have a GDP per capita above 50,000.

There we go! We’re well on our way to working with filtered views in Pandas.

MANIPULATE DATA WITH CALCULATIONS

What would Excel be without functions that help you calculate different results?

Pandas in this case leans heavily on the numpy library and general Python syntax to put calculations together. We’re going to go through a simple series of calculations on the GDP dataset we’ve been working on. Let’s for example, calculate the sum total of all GDP per capita countries that are over 50,000.

gdp50000.gdp_per_capita.sum()

That’ll give you the answer of 770046. Using that same logic we can calculate all sorts of things — the full list can be located at the Pandas documentation under the computation/descriptive statistics section located on the menu bar at the left.

DATA VISUALIZATION (CHARTS/GRAPHS)

Data visualization is a very powerful tool — it allows you to share insights you’ve gained with others in an accessible format. A picture, after all, is worth a thousand words. SQL and Excel both have the capability to translate queries into charts and graphs. With the seaborn and matplotlib libraries, you can do the same with Python.

There are far more comprehensive tutorials on data visualization options — a favorite of mine is this Github readme document (all in text) which explains how to build probability distributions and a wide variety of plots in Seaborn. That should give you an idea of how powerful data visualization can be in Python. If you’re ever feeling overwhelmed, you can use a solution such as Plot.ly which might be more intuitive to grasp.

We’re not going to go through each and every data visualization option — suffice it to say that with Python, you’re going to have a lot more power to visualize things than anything SQL can offer, and you’ll have to trade-off the additional flexibility you gain with Python for how easy it is in Excel for generating charts from templates.

In this case, we’re going to build a simple histogram to show the distribution of GDP per capita for those countries that have more than $50,000 in GDP per capita.

gdp50000.hist()

With this powerful histogram function (hist()) we can now generate a histogram that shows that most of the countries with a high GDP per capita cluster around the $50000 to $70000 range!

GROUPING AND JOINING DATA TOGETHER

Within Excel and SQL, powerful tools such as the JOIN function and pivot tables allow for the rapid aggregation of data.

Pandas and Python share many of the same functions that have been ported over from both SQL and Excel. You’ll be able to group data within datasets and join different datasets together. You can take a look here at the documentation. You’ll find that the join functionality offered by the merge function in Pandas is very similar to the one offered by SQL through the join command, while Pandas also offers pivot table functionality for those who are used to it in Excel.

We’re going to do a simple join here between the table we’ve developed with GDP per capita, and a list of world development indices from the World Bank.

Let’s first import the csv of country-level indicators.

country = pd.read_csv("Country.csv")

Let’s do a quick .head() function to take a look at the different columns in this dataset.

Now that we’re done, we can take a quick look and see that we’ve added a few columns that we can play with, including different years where data was sourced.

We can now see the table incorporates elements of both our GDP per capita column and our new country-wide table with different data columns. For those familiar with SQL joins, you can see that we’re doing an inner join on the Country column of our original dataframe.

Now that we have a joined table, we may want to group countries and their GDP per capita by the region of the world they’re in.

What if we want to see a permanent view of groupby summation? Groupby operations create a temporary object that can be manipulated, but they don’t create a permanent interface to aggregated results that can be built upon. For that, we’ll have to go through an old favorite of Excel users: the pivot table. Fortunately, pandas has a robust pivot table function.

Now we can see that the GDP per capita differs depending on the regions in different parts of the world. We have a clean table with the data we want.

—

This is a very superficial analysis: you’d want to actually do a weighted mean since a GDP per capita for each nation is not representative of the GDP per capita of every nation in a group since populations differ across the nations within a group.

In fact, you’ll want to redo all of our calculations involving means to reflect a population column for each country! See if you can do that within the Python notebook you’ve just started. If you can figure it out, you’ll have been well on your way to transferring your SQL or Excel knowledge to Python.

Got any comments or questions? Please leave them in the comments section on this blog post 🙂

Python list comprehension: an introduction and 5 great tips to learn

Python list comprehension empowers you to do something productive with code. This applies even if you’re a total code newbie. At code(love), we’re all about teaching you how to code and embrace the future, but you should never use technology just for its own sake.

Python list comprehension allows you to do something useful with code by filtering out certain values you don’t need in your data and changing lists of data to other lists that fit specifications you design. Python list comprehension can be very useful and it has many real-world applications: it is technology that can add value to your work and your day-to-day.

To start off, let’s talk a bit more about Python lists. A Python list is an organized collection of data. It’s perhaps easiest to think of programming as, among other things, the manipulation of data with certain rules. Lists simply arrange your data so that you can access them in an ordered fashion.

Let’s create a simple list of numbers in Python.

numbers=[5,34,324,123,54,5,3,12,123,657,43,23]print(numbers)

[5, 34, 324, 123, 54, 5, 3, 12, 123, 657, 43, 23]

You can see that we have all of the values we put into the variable numbers neatly arranged and accessible at any time. In fact, we can access say, the fifth variable in this list (54) at any time with Python list notation, or we can access the first 5 and last 5 values in the list.

Python list comprehensions are a way to condense Python for loops into lists so that you apply a formula to each value in the old list to create a new one. In other words, you loop a formula or a set of formulae to create a new list from an old one.

What can Python list comprehensions do for you?

Here’s a simple example where we filter out exactly which values in our numbers list are below 100. We start by applying the [ bracket, then add the formula we want to apply(x < 100) and the values we want to apply it to for(x in numbers -> numbers being the list we just defined). Then we close with a final ] bracket.

Now we can only display which values are below 100 in our list and filter out the rest with an if filter implemented in the next, which is followed by the if trigger.

lessthan100values=[xforxinnumbersifx<100]print(lessthan100values)

[5, 34, 54, 5, 3, 12, 43, 23]

We can do all sorts of things with a list of numbers with Python list comprehension.

We can add 2 to every value in the numbers list with Python list comprehension.

plus2=[x+2forxinnumbers]print(plus2)

[7, 36, 326, 125, 56, 7, 5, 14, 125, 659, 45, 25]

We can multiply every value by 2 in the numbers list with Python list comprehension.

multiply2=[x*2forxinnumbers]print(multiply2)

[10, 68, 648, 246, 108, 10, 6, 24, 246, 1314, 86, 46]

And this isn’t just restricted to numbers: we can play with all kinds of data types such as strings of words as well. Let’s say we wanted to create a list of capitalized words in a string for the sentence “I love programming.”

Hopefully by now, you can grasp the power of Python list comprehension and how useful it can be. Here are 5 tips to get you started on learning and playing with data with Python list comprehensions.

1) Have the right Python environment set up for quick iteration

When you’re playing with Python data and building a Python list comprehension, it can be hard to see what’s going on with the standard Python interpreter. I recommend checking out iPython Notebook: all of the examples in this post are written in it. This allows you to quickly print out and change list comprehensions on the fly. You can check out more tips on how to get the right Python setup with my list of 11 great resources to learn and work in Python.

2) Understand how Python data structures work

In order for you to really work with Python list comprehensions, you should understand how data structures work in Python. In other words, you should know how to play with your data before you do anything with it. The official documentation on the Python website for how you can work with data in Python is here. You can also refer again to our resources on Python.

3) Have real-world data to play with

I cannot stress enough that while a Python list comprehension is useful even with pretend examples, you’ll never really understand how to work with them and get things done until you have a real-world problem that requires list comprehensions to solve.

Many of you came to this post with something you thought list comprehensions could solve: that doesn’t apply to you. If you’re one of those people who are looking to get ahead and learn without a pressing problem, do look at public datasets filled with interesting data. There’s even a subreddit filled with them!

Real-world data with code(love)

4) Understand how to use conditionals in list comprehensions

One of the most powerful applications of Python list comprehensions is the ability to be able to selectively apply different treatments to different values in a list of values. We saw some of that power in some of our first examples.

If you can use conditionals properly, you can filter out values from a list of data and selectively apply formulas of any kind to different values.

In this example, we first find every square power of the range of numbers from 1 to 20 with a list comprehension.

Then we can filter which ones are even by adding in a conditional that only returns TRUE for values that when divided by 2 return a remainder of 0 (even numbers, in other words).

We can then combine the two into one list comprehension.

square20combined=[x**2forxinrange(21)ifx%2==0]print(square20combined)

[0, 4, 16, 36, 64, 100, 144, 196, 256, 324, 400]

Sometimes, it’s better not to do this if you want things to be more readable for your future self and any audience you’d like to share your code with, but it can be more efficient.

5) Understand how to nest list comprehensions in list comprehensions and manipulate lists with different chained expressions

The power of list comprehensions doesn’t stop at one level. You can nest list comprehensions within list comprehensions to make sure you chain multiple treatments and formulae to data easily.

At this point, it’s important to understand just what list comprehensions do again. Because they’re condensed for loops for lists, you can think about how combining outer and inner for loops together. If you’re not familiar with Python for loops, please read the following tutorial.

If we were to represent this as a series of Python for loops instead, it might be easier to grasp the logic of a Python list comprehension. As we move from the outer loop to the inner loop, what happens is that for each x value from 1 to 9 (for x in range(1,10)), we print out a range of values from 0 to x.

The chain of for loops we just went over has the exact same logic as our initial list comprehension. You’ll notice though that in a for loop, you will print seperate values while in a list comprehension it will produce a new list, which allows us to use Python list notation to play with the data.

With this in mind, you can make your code more efficient and easily manipulable with a Python list comprehension.

I hope you enjoyed my introduction to Python List Comprehensions. If you want to check out more content on learning code, check out the rest of my content at code-love.com! Please comment if you want to join the discussion, and share if this created value for you 🙂

I’ve always had a theory that Newton’s Third Law applied to people. I think that’s largely been borne out by all that I’ve experienced.

Put your trust in people and you’ll get that trust repaid in you.

Do good, and others will do good unto you.

It was Dale Carnegie’s How to Win Friends and Influence People that probably put it best—when you make people feel like they’re important, they’ll respond to you in kind. You shouldn’t ask people for things directly: you should ask them for a favor, make sure that they know that they’re in a position of influence and power. That will elicit a favorable response to your endeavors.

I’ve always been a huge fan of involving people in creative projects, getting people to feel like they have ownership over something even if it wasn’t them that originated the idea. I think that’s the basis of the greatest way to grow something: when people push your idea as if it was their own. Then, if you can get them to join up with you for the long haul by joining your mailing list, you’ll be able to grow your own brand as well.

If people believe that you’ve heeded their words, and that they’ve played a part in your creative process, people will act on your behalf. If you genuinely listen to the input people give you, that’ll make them feel even more inclined to help spread the word.

I’ve always loved testing this—collecting both information that shapes my creative projects, and fans is a strong ideal for me. I worked for a startup where I segmented all of their top users, and surveyed them about what they actually used it for. Those super-users not only gave a ton of insight—they re-activated and used the product again at higher rates.

So while I was preparing for the launch of my book, I wondered how I could get people involved. Finally, it hit me: I was missing the most important element of my book: the title.

So I set off to create a survey form and prepare a list of startup Facebook groups I wanted to ask for their insights. Here’s what the form looked like:

I collected the answers on a Google spreadsheet connected to the form. Technologically, it took me about 5 minutes to get this all set up.

I then spent a couple of hours sending the link to this form on my personal social media profiles, and on a few selected startup Facebook groups. The results were convincing—

I got 369 clicks. Out of those 369, 167 replied with a title (conversion rate of 45.25%), and 133 asked to be part of the mailing list for when the book launched (conversion rate of 36%).

The click-through rate on the first newsletter I sent out was 20%, leading me to believe that upwards of about 30 people got wind of my book from just a couple of hours of work. I did get a few unsubscribes, but many people opted to stay on my mailing list: so I added a pool of new subscribers interested in what I do. I was able to grow my mailing list very rapidly.

I got great data from the 167 responses.

A significant amount of people chose the other option, which meant my titles I thought up were not strong enough. In a comment on one of the Facebook threads, someone came up with the idea that I should title the book Startup Blackjack since I was defining 21 startup terms—I ended up titling the book Entrepreneur Blackjack: 21 Startup Buzzwords Defined. This was also because titles with the word “Defined” scored high in the chosen categories—and so did the word bulls**t, even if I didn’t incorporate that (it might be something where profanity does drive sales, but I was giving my parents the book, so it wasn’t the best choice for me.)

Why did this happen?

I believe it’s because I really tapped into the entrepreneurs I asked ,making them feel part of the creative process. With two simple call-to-action prompts, people could contribute their input, then buy into the project they just helped create. For me it was a win-win-win—I got more people interested in the book, people who wanted the information I collected got access, and I created the title of the book with customer validation rather than blind guessing. I was able to grow my mailing list with new fans who were engaged straight off the bat.

With every action, there’s an equal reaction. I believed in creating with others—and I was happy to see others wanted to create with me. Here’s to your efforts to activating that same feeling.

Comment below if you’ve ever done something like this, or are giving this a try. I want to learn with you.

Want to learn more? Check out our other learning guides, and our learning lists filled with free resources to learn technology and entrepreneurship!

In an online chat session between Yukihiro Matsumoto and Keiju Ishitsuka in early 1993, a discussion ensued about the name of a programming language that Matsumoto was going to write. He wanted to satisfy his desire to have an object-oriented scripting language, something that would craft virtual objects composed of data, and help them interact with one another. The alternatives at the time, Python and Perl didn’t appeal to him, Python being too object-oriented and Perl having “the smell of a toy language”. Between “Coral” and “Ruby”, Matsumoto decided to go with the latter because it was the birthstone of one of his colleagues.

You have probably heard about Ruby, and you might be wondering—what is all the fuss?

For starters, it’s written in a very easy-to-use, intuitive manner.

For beginners who have tried teaching themselves a programming language, there are many obvious barriers like the syntax and semantics of a language. Ruby strives to eliminate some of those barriers, for example, by naming functions in a very “natural-language” like format, the is_a? function does exactly what it promises, returning a Boolean (TRUE or FALSE) telling you whether a given object is of a certain type. The question mark at the end of the function is a Ruby idiosyncrasy that hints that the function always returns a Boolean. It may seem odd in the beginning, but as the amount of Ruby you read increases, the more natural this process will become.

Ruby is widely deployed ranging from applications in simulations, 3D modeling, business, robotics to web applications and security. For example, Basecamp – a project management application is programmed entirely in Ruby. Google SketchUp, a 3D modeling tool uses Ruby as its macro scripting API—programmers can add in scripts of their own to the SketchUp program, helping them do things such as automating routine modelling processes, similar to how macros work in Excel.

So how might you go about to learn Ruby, now that you are convinced that it is valued by the software community?

RubyMonk follows a narrative style of teaching Ruby along with some programming basics. The premise is based on you having a “master” who gives you much needed encouragement if you go wrong and also gives you triumphant messages when you succeed at some of the exercises. RubyMonk draws from movies, and video games to keep you plugging away to learn Ruby.

What really makes it stand apart from other resources is the way the entire learning environment is structured. Each page in the chapter has some introduction, a new concept, an exercise to try out, some more concepts with exercises and wrapping it up by using all the elements learned in that chapter in a slightly challenging exercise. There are several levels – Ruby Primer, Ruby Primer: Ascent, Metaprogramming Ruby and Metaprogramming Ruby: Ascent.

Each of the levels deliver content indicative of their name and each chapter is sprinkled with practical exercises.The design of the exercises and their placement is what makes the learning experience on this website fun and engaging. The exercises are just a little beyond the skill level you acquired in the lesson and require a little bit of thinking and are perfect for people who are just beginning to learn programming. They help easily transfer the theory you learned into practice.

Once, you’re done going through all of their material, you can be fairly confident that even if you can’t change the world with Ruby, you’ll at least have enough knowledge to create fun programs and venture into some complex ones with little additional effort.

Yet another reason to learn: Ruby serves as a wonderful background to migrate to the popular Ruby on Rails web framework, which makes the learning curve for making web applications much easier.

Ruby on Rails was constructed with the explicit goal of making it as easy as possible to build an interactive web platform, and maintain it. It speaks to the Ruby philosophy of simple, intuitive building.

After you learn Ruby, you will be able to build your ideas rapidly, and efficiently. You will have learned a valuable skill that will help make building natural.